Abstract 352: Predictive Value of Discharge Neurological Outcome Scores Following Out-of-Hospital Cardiac Arrest for Long-term Neurological Status: A Systematic Review
Bibliographic record
Abstract
Introduction: Neurological outcomes following out-of-hospital cardiac arrest are commonly assessed using clinically validated outcome measures such as the Cerebral Performance Category (CPC) score, and are mainstay for evaluating neurological status at discharge. However, it remains unclear if these measures accurately reflect long-term neurological status after discharge. The primary objective of this systematic review was to better understand the predictive value of discharge neurological outcome scores for long-term neurological status. Methods: Comprehensive electronic searches of Medline, Embase and The Cochrane Library from inception to September 2016 were conducted and reference lists were hand-searched.Randomized controlled trials (RCT) and prospective observational studies were included. Our primary outcome was the correlation between discharge or 30 days post-arrest neurologic status and long-term ( > 3 month) neurological outcome score. Preliminary Results: After screening 4,265 titles and abstracts independently and in duplicate, 6 studies including 5 prospective observational studies and 1 RCT were included. Four studies reported long-term follow-up at 6 months post-arrest and 2 studies reported follow-up at 1 year. In the studies with 6-month follow-up, 368/450 patients (82.7%) had favourable short-term neurological scores (CPC 1-2) at discharge or 30 days post-arrest, and 352/445 patients (79.1%) had favourable scores at 6 months post-arrest. In the studies with 1-year follow-up, 67/80 patients (83.8%) had favourable neurological scores at discharge or 30 days post-arrest, and 60/80 patients (75%) patients had favourable neurological scores at 1 year. Conclusion: Long-term neurological outcome scores following OHCA were consistent with short-term outcome at hospital discharge or 30 days post-arrest. Further studies are needed to elucidate more comprehensive prognostic factors for predicting long-term neurological outcome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".